The rise of the Modern Data Stack and the Modern Data Quality Platform

The data producers, consumers, and leaders deserve an ecosystem that delivers the data that is relevant to them – one size fits all approaches and solutions no longer cut it in this modern data landscape. Data minds and Business minds see data in different ways even when working on the same data for the same business outcomes. You need a platform that promotes Decentralized Data ownership culture to improve data relevance and data collaboration.

Abstract:

With the growth in data, there is an explosion of modern architectural thinking (Data as Product, Multi-Cloud) that has led us to Cloud Datawarehouse, Lakehouse to Data Fabric, and Data Mesh adoptions. With this growth and expansion of technologies, both the Data Producers and Consumers of data are shifting away from the traditional ideologies around Centralized Data Ownership towards new principles around “Decentralized Data Ownership”. Further, requires a tight collaboration across different persona to meet business needs and the data quality needs.

This requires not just looking at metadata but going beyond metadata and looking under the data to derive insights from top to bottom and tying directly to business outcomes.  We at DQLabs believe that a comprehensive modern data quality check should go across these three levels – Data Reliability, Business Fit Measures, and KPI metrics.

In our second installment of the Defining Data Relevance webinar series, Raj Joseph, Founder and CEO of DQLabs, and Sanjeev Mohan, industry expert and Principal at SanjMo, unpack the complexities of the Modern Data Stack and Modern Data Quality Platforms and the ever-growing need for Data Relevance.